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Sparse and robust support vector machine with capped squared loss for large-scale pattern classification
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AI
DOI：10.1016/j.patcog.2024.110544.png)
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摘要
En 中文
Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slow. To address this challenge, we construct a novel sparse and robust SVM based on our newly proposed capped squared loss (named as L-csl-SVM). To solve L-csl-SVM, we first focus on establishing optimality theory of L-csl-SVM via our defined proximal stationary point, which is convenient for us to efficiently characterize the L-csl support vectors of L-csl-SVM. We subsequently demonstrate that the L-csl support vectors comprise merely a minor fraction of entire training data. This observation leads us to introduce the concept of the working set. Furthermore, we design a novel subspace fast algorithm with working set (named as L-csl-ADMM) for solving L-csl-SVM, which is proven that L-csl-ADMM has both global convergence and relatively low computational complexity. Finally, numerical experiments show that L-csl-ADMM has excellent performances in terms of getting the best classification accuracy, using the shortest time and presenting the smallest numbers of support vectors when solving large-scale pattern classification problems.
Keyword:
Capped squared loss
Fast algorithm
Support vectors
Low computational complexity
Working set
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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